In the race to make artificial intelligence faster, more efficient, and widely accessible, the spotlight often falls on GPUs. They dominate AI inference and training, but their prominence leaves an important question: what happens outside GPU-driven systems? As organizations look for scalable solutions that don’t rely on costly or limited hardware, CPUs are stepping back into focus. Optimizing AI workloads on these non-GPU architectures is no longer optional; it’s becoming essential. This isn’t only about performance gains; it’s about making AI accessible and effective across a broader range of platforms. This is where Rajalakshmi Srinivasaraghavan has built her expertise. A researcher and engineer with deep roots in high-performance computing, she has dedicated her career to refining AI inference on CPUs. Her journey blends technical breakthroughs with community-driven collaboration. “By collaborating with open-source communities, we were able to publish cutting-edge optimizations that accelerated performance across AI workloads,” she says. “This effort strengthened the ecosystem as a whole and positioned our team as a key player in scalable, high-performance computing.” One of her standout achievements has been in CPU optimization strategies. While many in the field concentrate on GPU-centric approaches, Rajalakshmi has shown how targeted CPU improvements deliver real results. Her team prioritized key software packages for CPU optimization, producing significant performance boosts. “We achieved up to a 50% performance improvement on next-generation hardware by identifying and optimizing critical AI workflows,” she explains. These results prove that organizations can stretch existing resources while preparing for the next wave of AI deployment. Her influence inside organizations reaches beyond coding. She led the rollout of continuous integration (CI) builds across a wide set of packages, automating testing and integration. “This automation improved reliability and freed up resources to focus on core development,” she says. The impact was immediate: faster innovation cycles and software optimizations that could keep up with evolving hardware demands. In an industry where speed often defines success, her approach helped teams move quickly and smarter. Rajalakshmi also values mentorship as part of her work. She has guided new engineers, passing on the technical and strategic insights needed for CPU optimization. “Empowering new members ensured that performance improvements were not just one-off wins but part of a long-term trajectory,” she reflects. Her focus on nurturing talent created stronger teams and a culture of sustainable innovation, an asset in the competitive space of high-performance computing. Her ability to think ahead has also set her apart. She tackled one of the industry’s recurring hurdles by optimizing and validating code in simulators before hardware release. This proactive approach allowed teams to catch problems early and launch fully functional software stacks alongside new hardware. “It meant our software stack was fully functional and production-ready on day one,” she says. Few teams manage this, and the achievement cut delays while ensuring immediate performance on next-generation systems. The impact extends into research and academia as well. Papers such as “Modeling Matrix Engines for Portability and Performance” and “A Matrix Math Facility for Power ISA Processors” highlight her commitment to bridging theory with practical applications. By contributing to both research and industry discussions, she shapes the broader dialogue on AI efficiency. Looking forward, she sees growing demand for scalable solutions that move beyond GPUs. She believes the future lies in tighter alignment between hardware and software. “By closely tracking industry trends and anticipating shifts, we integrated forward-looking capabilities into hardware and software,” she says. For her, the next generation of AI will require efficiency built in at every level, giving platforms that embed optimization a clear competitive edge. She underscores the need for the industry to embrace a more inclusive view of AI infrastructure. Optimizing inference on CPUs and other non-GPU architectures is not a backup plan; it’s a way to build more affordable, resilient, and accessible AI ecosystems. As she concludes, “The foundation for long-term success in AI lies in building solutions that evolve with hardware and scale with demand.” In a world fixated on GPUs, Rajalakshmi shows us that real innovation often grows in overlooked spaces. She turns her focus to CPUs, opening new possibilities for AI deployment and building a future where AI feels more equitable, efficient, and sustainable. This story was distributed as a release by Kashvi Pandey under HackerNoon’s Business Blogging Program. This story was distributed as a release by Kashvi Pandey under HackerNoon’s Business Blogging Program.

This story contains new, firsthand information uncovered by the writer.

Optimizing AI Inference on Non-GPU Architectures by Rajalakshmi Srinivasaraghavan

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